GPT-5.1-Codex-Max vs Mistral Medium 3
Mistral Medium 3 costs 5.0x less per output token than GPT-5.1-Codex-Max ($2.00 vs $10.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
GPT-5.1-Codex-Max or Mistral Medium 3: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Max for a larger context window (400K against 131K tokens).
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Mistral Medium 3 on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and LMArena Text: coding. Compare them on price and context instead.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
No call
Only Mistral Medium 3 has a AA Intelligence Index result, 9.0.
Directional only
Web design
Design Arena: websites
No call
Only Mistral Medium 3 has a Design Arena: websites result, 1079.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Max
GPT-5.1-Codex-Max takes 400K tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mistral Medium 3
GPT-5.1-Codex-Max $0.0063, Mistral Medium 3 $0.0014 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Max not benchmarked, Mistral Medium 3 not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-5.1-Codex-Max $10.00 against Mistral Medium 3 $2.00 per million output tokens, 5.0x apart.
- Context window
- GPT-5.1-Codex-Max takes 400K tokens, Mistral Medium 3 131K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards measured
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3 | Leader |
|---|---|---|---|
| LiveCodeBench (Vals AI) | 83.6% | Not measured | Only GPT-5.1-Codex-Max measured |
| Vibe Code Bench (Vals AI) | 22.2% | Not measured | Only GPT-5.1-Codex-Max measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 40.0% | Only Mistral Medium 3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 3.8% | Only Mistral Medium 3 measured |
| Terminal-Bench | 60.4% | Not measured | Only GPT-5.1-Codex-Max measured |
| ALE-Bench | 1228.3 | Not measured | Only GPT-5.1-Codex-Max measured |
| LMArena Text: coding | Not measured | 1385 | Only Mistral Medium 3 measured |
| LMArena Text: software and IT | Not measured | 1385 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 9.0 | Only Mistral Medium 3 measured |
| Epoch Capabilities Index | Not measured | 134.0 | Only Mistral Medium 3 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 24.3% | Only Mistral Medium 3 measured |
| METR time horizons | 70.8% | Not measured | Only GPT-5.1-Codex-Max measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 57.8% | Only Mistral Medium 3 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 4.1% | Only Mistral Medium 3 measured |
| LMArena Text: hard prompts | Not measured | 1364 | Only Mistral Medium 3 measured |
| LMArena Text: expert | Not measured | 1342 | Only Mistral Medium 3 measured |
| LMArena Text: hard prompts in English | Not measured | 1374 | Only Mistral Medium 3 measured |
| LMArena Vision: diagrams | Not measured | 1155 | Only Mistral Medium 3 measured |
| LMArena Vision: homework | Not measured | 1165 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 15 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 30.3 | Only Mistral Medium 3 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 30.3% | Only Mistral Medium 3 measured |
| ProofBench | 9.0% | Not measured | Only GPT-5.1-Codex-Max measured |
| LMArena Text: math | Not measured | 1353 | Only Mistral Medium 3 measured |
| LMArena Text: mathematical work | Not measured | 1354 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 76.0% | Only Mistral Medium 3 measured |
| LMArena Text: science | Not measured | 1381 | Only Mistral Medium 3 measured |
| LMArena Text: medicine and healthcare | Not measured | 1381 | Only Mistral Medium 3 measured |
| LMArena Text: legal and government | Not measured | 1384 | Only Mistral Medium 3 measured |
| LMArena Text: business and finance | Not measured | 1352 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1079 | Only Mistral Medium 3 measured |
| Design Arena: UI components | Not measured | 1024 | Only Mistral Medium 3 measured |
| Design Arena: game dev | Not measured | 1018 | Only Mistral Medium 3 measured |
| Design Arena: data viz | Not measured | 1034 | Only Mistral Medium 3 measured |
| Design Arena: 3D | Not measured | 1084 | Only Mistral Medium 3 measured |
| Design Arena: all code categories | Not measured | 1069 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index28 boards measured
| Benchmark | GPT-5.1-Codex-Max | Mistral Medium 3 | Leader |
|---|---|---|---|
| IFBench | Not measured | 39.3% | Only Mistral Medium 3 measured |
| Long context reasoning (AA-LCR) | Not measured | 31.3% | Only Mistral Medium 3 measured |
| DTBench | Not measured | 62.3% | Only Mistral Medium 3 measured |
| LMArena Text | Not measured | 1370 | Only Mistral Medium 3 measured |
| LMArena Text: instruction following | Not measured | 1338 | Only Mistral Medium 3 measured |
| LMArena Text: creative writing | Not measured | 1343 | Only Mistral Medium 3 measured |
| LMArena Vision | Not measured | 1157 | Only Mistral Medium 3 measured |
| LMArena Text: English | Not measured | 1385 | Only Mistral Medium 3 measured |
| LMArena Text: non-English | Not measured | 1353 | Only Mistral Medium 3 measured |
| LMArena Text: Chinese | Not measured | 1374 | Only Mistral Medium 3 measured |
| LMArena Text: German | Not measured | 1377 | Only Mistral Medium 3 measured |
| LMArena Text: Japanese | Not measured | 1316 | Only Mistral Medium 3 measured |
| LMArena Text: Korean | Not measured | 1304 | Only Mistral Medium 3 measured |
| LMArena Text: French | Not measured | 1368 | Only Mistral Medium 3 measured |
| LMArena Text: Spanish | Not measured | 1364 | Only Mistral Medium 3 measured |
| LMArena Text: Russian | Not measured | 1359 | Only Mistral Medium 3 measured |
| LMArena Text: Polish | Not measured | 1371 | Only Mistral Medium 3 measured |
| LMArena Text: multi-turn | Not measured | 1384 | Only Mistral Medium 3 measured |
| LMArena Text: longer queries | Not measured | 1360 | Only Mistral Medium 3 measured |
| LMArena Text: writing and language | Not measured | 1352 | Only Mistral Medium 3 measured |
| LMArena Text: entertainment and media | Not measured | 1335 | Only Mistral Medium 3 measured |
| LMArena Vision: English | Not measured | 1172 | Only Mistral Medium 3 measured |
| LMArena Vision: Chinese | Not measured | 1161 | Only Mistral Medium 3 measured |
| LMArena Vision: reading text | Not measured | 1154 | Only Mistral Medium 3 measured |
| LMArena Vision: humor | Not measured | 1145 | Only Mistral Medium 3 measured |
| LMArena Vision: captioning | Not measured | 1094 | Only Mistral Medium 3 measured |
| LMArena Vision: recognizing things | Not measured | 1141 | Only Mistral Medium 3 measured |
| LMArena Vision: creative writing | Not measured | 1146 | Only Mistral Medium 3 measured |
| Neither model has a result on the other 18 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Mistral Medium 3 costs less
Repository review
50K input + 3K output tokens
Mistral Medium 3 costs less
Agent loop
200K input + 10K output tokens
Mistral Medium 3 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-5.1-Codex-Max | Mistral Medium 3 |
|---|---|---|
| Maker | OpenAI | Mistral AI |
| Weights | No public weights listed | No public weights listed |
| Released | December 4, 2025 | May 7, 2025 |
| Context window | 400K | 131K |
| Input $/Mtok | $1.25 | $0.40 |
| Output $/Mtok | $10.00 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Max better than Mistral Medium 3 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Max for a larger context window (400K against 131K tokens).
Which is cheaper, GPT-5.1-Codex-Max or Mistral Medium 3?
GPT-5.1-Codex-Max costs $1.25 per million input tokens and $10.00 per million output tokens; Mistral Medium 3 costs $0.40 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Max or Mistral Medium 3?
GPT-5.1-Codex-Max, with 400K tokens against 131K.
Has any benchmark tested both GPT-5.1-Codex-Max and Mistral Medium 3?
No benchmark has tested both yet. GPT-5.1-Codex-Max has results on LiveCodeBench (Vals AI), Vibe Code Bench (Vals AI) and Terminal-Bench; Mistral Medium 3 on LiveCodeBench (Artificial Analysis run), Terminal-Bench Hard (Artificial Analysis run) and LMArena Text: coding. Compare them on price and context instead. Data as of October 21, 2026.